KMeans

Defined in: ds/src/ml/estimators/KMeans.js:17

Extends

• Estimator

Constructors

Constructor

new KMeans(params?): KMeans

Defined in: ds/src/ml/estimators/KMeans.js:21

Parameters

params?

any = {}

{ k, maxIter, tol, seed }

Returns

KMeans

Overrides

Estimator.constructor

Properties

_state

_state: object

Defined in: ds/src/core/estimators/estimator.js:27

Inherited from

Estimator._state

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_warnings

_warnings: any[]

Defined in: ds/src/core/estimators/estimator.js:29

Inherited from

Estimator._warnings

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centroids

centroids: any

Defined in: ds/src/ml/estimators/KMeans.js:85

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converged

converged: any

Defined in: ds/src/ml/estimators/KMeans.js:88

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fitted

fitted: boolean

Defined in: ds/src/ml/estimators/KMeans.js:30

Inherited from

Estimator.fitted

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inertia

inertia: any

Defined in: ds/src/ml/estimators/KMeans.js:86

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iterations

iterations: any

Defined in: ds/src/ml/estimators/KMeans.js:87

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k

k: any

Defined in: ds/src/ml/estimators/KMeans.js:23

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labels

labels: any

Defined in: ds/src/ml/estimators/KMeans.js:84

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maxIter

maxIter: any

Defined in: ds/src/ml/estimators/KMeans.js:24

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model

model: any

Defined in: ds/src/ml/estimators/KMeans.js:29

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params

params: any

Defined in: ds/src/core/estimators/estimator.js:24

Inherited from

Estimator.params

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seed

seed: any

Defined in: ds/src/ml/estimators/KMeans.js:26

---

tol

tol: any

Defined in: ds/src/ml/estimators/KMeans.js:25

Methods

_prepareArgsForFit()

_prepareArgsForFit(args?): { columns?: undefined; columnsX: any[]; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y: any[]; } | { columns: any[]; columnsX?: undefined; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y?: undefined; } | { columns?: undefined; columnsX?: undefined; prepared?: undefined; raw: any[]; rows?: undefined; X?: undefined; y?: undefined; }

Defined in: ds/src/core/estimators/estimator.js:367

Convenience helper: parse arguments passed to fit/predict/transform.

Supports declarative table-style inputs:

• fit({ X, y, data, omit_missing })
• fit({ data, columns, … })

Returns an object { X, y, prepared, rows } where X/y are numeric arrays if preparation was required, otherwise returns the original values.

Note: this helper only prepares numeric matrices/vectors using core table utilities; it does not perform encoding of categorical predictors.

Parameters

args?

any[] = []

Returns

{ columns?: undefined; columnsX: any[]; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y: any[]; } | { columns: any[]; columnsX?: undefined; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y?: undefined; } | { columns?: undefined; columnsX?: undefined; prepared?: undefined; raw: any[]; rows?: undefined; X?: undefined; y?: undefined; }

Inherited from

Estimator._prepareArgsForFit

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_repr_html_()

_repr_html_(): string

Defined in: ds/src/core/estimators/estimator.js:201

Observable/Jupyter HTML representation

Returns

string

HTML representation

Inherited from

Estimator._repr_html_

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clearWarnings()

clearWarnings(): void

Defined in: ds/src/core/estimators/estimator.js:139

Clear all warnings

Returns

void

Inherited from

Estimator.clearWarnings

---

fit()

fit(X, opts?): KMeans

Defined in: ds/src/ml/estimators/KMeans.js:49

Fit the KMeans model.

Accepts:

• numeric input: fit(Xarray, { k, maxIter, tol, seed })
• declarative input: fit({ data: tableLike, columns: [‘c1’,‘c2’], k, … })

Returns this.

Parameters

X

any

Feature matrix (n samples × p features), or a declarative options object ({ data, columns, k, … }).

opts?

Optional fitting overrides for the positional numeric form.

k?

number

Number of clusters.

maxIter?

number

Maximum number of iterations.

seed?

number

Random seed for centroid initialization.

tol?

number

Convergence tolerance.

Returns

KMeans

The fitted estimator (for chaining).

Overrides

Estimator.fit

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getMemoryUsage()

getMemoryUsage(): string

Defined in: ds/src/core/estimators/estimator.js:97

Get memory usage in human-readable format

Returns

string

Memory usage string (e.g., “2.3 MB” or “145 KB”)

Inherited from

Estimator.getMemoryUsage

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getParams()

getParams(): any

Defined in: ds/src/core/estimators/estimator.js:294

Get a shallow copy of parameters.

Returns

any

Inherited from

Estimator.getParams

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getState()

getState(): any

Defined in: ds/src/core/estimators/estimator.js:65

Get comprehensive model state

Returns

any

State information including fitted status, memory estimate, warnings

Inherited from

Estimator.getState

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getWarnings()

getWarnings(): any[]

Defined in: ds/src/core/estimators/estimator.js:124

Get all warnings

Returns

any[]

Array of warning objects

Inherited from

Estimator.getWarnings

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getWarningsByType()

getWarningsByType(type): any[]

Defined in: ds/src/core/estimators/estimator.js:148

Get warnings of a specific type

Parameters

type

string

Warning type

Returns

any[]

Filtered warnings

Inherited from

Estimator.getWarningsByType

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hasWarnings()

hasWarnings(): boolean

Defined in: ds/src/core/estimators/estimator.js:132

Check if model has warnings

Returns

boolean

Inherited from

Estimator.hasWarnings

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isFitted()

isFitted(): boolean

Defined in: ds/src/core/estimators/estimator.js:36

Check if model is fitted

Returns

boolean

Inherited from

Estimator.isFitted

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predict()

predict(X): number[]

Defined in: ds/src/ml/estimators/KMeans.js:103

Predict cluster labels for new data.

Accepts:

• numeric array: predict([[x1,x2], [x1,x2], …])
• declarative: predict({ data: tableLike, columns: [‘c1’,‘c2’], omit_missing: true })

Parameters

X

any

Feature matrix to assign, or a declarative options object ({ data, columns, … }).

Returns

number[]

Predicted cluster labels (one integer index per sample).

Overrides

Estimator.predict

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save()

save(): string

Defined in: ds/src/core/estimators/estimator.js:329

Save model to JSON string

Returns

string

JSON representation of the model

Inherited from

Estimator.save

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setParams()

setParams(params?): KMeans

Defined in: ds/src/core/estimators/estimator.js:285

Set parameters (mutates instance).

Parameters

params?

any = {}

Returns

KMeans

Inherited from

Estimator.setParams

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silhouetteScore()

silhouetteScore(X, labels?): number

Defined in: ds/src/ml/estimators/KMeans.js:127

Compute silhouette score for given X and labels (or use fitted labels if omitted).

Accepts:

• numeric X array, and optional labels array
• declarative object { data, columns } will be prepared

Parameters

X

any

labels?

any = null

Returns

number

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summary()

summary(): object

Defined in: ds/src/ml/estimators/KMeans.js:149

Convenience: return summary stats for fitted model

Returns

object

centroids

centroids: any

converged

converged: any

inertia

inertia: any

iterations

iterations: any

k

k: any

---

toJSON()

toJSON(): object

Defined in: ds/src/ml/estimators/KMeans.js:163

Serialization helper

Returns

object

__class__

__class__: string = 'KMeans'

fitted

fitted: boolean

model

model: any

params

params: any

Overrides

Estimator.toJSON

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transform()

transform(): void

Defined in: ds/src/core/estimators/estimator.js:431

Transform should be implemented by transformers.

Returns

void

Inherited from

Estimator.transform

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fromJSON()

static fromJSON(obj?): KMeans

Defined in: ds/src/ml/estimators/KMeans.js:172

Basic deserialization. Subclasses should override if they need to restore learned arrays / matrices.

Parameters

obj?

Returns

KMeans

Overrides

Estimator.fromJSON

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load()

static load(jsonString): Estimator

Defined in: ds/src/core/estimators/estimator.js:346

Load model from JSON string

Parameters

jsonString

string

JSON representation

Returns

Estimator

Reconstructed estimator instance

Inherited from

Estimator.load